Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images


We propose an end-to-end deep learning architecture that produces a 3D shape in triangular mesh from a single color image. Limited by the nature of deep neural network, previous methods usually represent a 3D shape in volume or point cloud, and it is non-trivial to convert them to the more ready-to-use mesh model. Unlike the existing methods, our network represents 3D mesh in a graph-based convolutional neural network and produces correct geometry by progressively deforming an ellipsoid, leveraging perceptual features extracted from the input image. We adopt a coarse-to-fine strategy to make the whole deformation procedure stable, and define various of mesh related losses to capture properties of different levels to guarantee visually appealing and physically accurate 3D geometry. Extensive experiments show that our method not only qualitatively produces mesh model with better details, but also achieves higher 3D shape estimation accuracy compared to the state-of-the-art.


Code and Dataset


Qualitative results. (a) Input image; (b) Volume from 3D-R2N2 [1], converted using Marching Cube [4]; (c) Point cloud from PSG [2], converted using ball pivoting [5]; (d) N3MR[3]; (e) Ours; (f) Ground truth.


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  2. Fan, H., Su, H., Guibas, L.J.: A point set generation network for 3d object reconstruction from a single image. In: CVPR. (2017)
  3. Kato, H., Ushiku, Y., Harada, T.: Neural 3d mesh renderer. In: CVPR. (2018)
  4. Lorensen, W.E., Cline, H.E.: Marching cubes: A high resolution 3d surface construction algorithm. In: SIGGRAPH. (1987)
  5. Bernardini, F., Mittleman, J., Rushmeier, H.E., Silva, C.T., Taubin, G.: The ball-pivoting algorithm for surface reconstruction. IEEE Trans. Vis. Comput. Graph. 5(4) (1999) 349–359